A Comparative Study of Supervised Machine Learning Algorithms for Fruit Prediction
Creators
- 1. UG Student, Department of Computer Engineering, RMD Sinhgad School of Engineering, Pune, Maharashtra, India
- 2. Professor, Department of Computer Engineering, RMD Sinhgad School of Engineering, Pune, Maharashtra, India
Description
In this paper, machine learning techniques have been applied for the fruit image classification and prediction over a large dataset. In the implemented work, five models have been developed and their performances are compared in predicting the fruit names. These models are based on five supervised learning techniques i.e., Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), Naive Bayes, and Softmax. The experimental results show that Support Vector Machine algorithm performs the best for large datasets and also Support Vector Machine is the best for small datasets. The results also reveal that reduction in the number of fruits reduces the accuracy’s of each algorithm.
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(14-18)A comparative study of Supervised.pdf
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Additional details
References
- Sa; Ge, Z.; Dayoub, F.; Upcroft, B.; Perez, T.; McCool, C. DeepFruits: A Fruit Detection System Using Deep Neural Networks. Sensors 2016, 16, 1222. I. Sa, Z. Ge, F. Dayoub, B. Upcroft, T. Perez and C. McCool, DeepFruits: A Fruit Detection System Using Deep Neural Networks, Sensors 2016, 16, 1222
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- B. Guo, S. R. Gunn, R. I. Damper and J. D. B. Nelson, Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification, IEEE Transactions on Image Processing, vol. 17, no. 4, pp. 622-629, April 2008
- R. Garreta and G. Moncecchi, Learning scikit-learn: Machine Learning in Python, ISBN - 978-1783281930
Subjects
- Applied Science Journals
- http://matjournals.com/Engineering-Journals.html